AI

Martijn Verbove@verboveAI · #3

Funny how the scope of a weekend project changes with Fable 5 — put together a nation-builder where every citizen is an AI you can actually talk to. How it works: 1. Start an island nation with 15 AI citizens 2. Govern by talking — build, tax, and settle disputes in your own words 3. Every promise you make is recorded, and the island remembers 4. Keep your word and trust grows. Break it and the story spreads 5. Grow your nation without losing your people — or face a coup

Demonstrates creative AI application with clear workflow

Peter Prins@PeterPrins10AI · #4

I asked both Fable and Sol to work on the same code base. Then I had both Fable and Sol review each other's code blindly. The results may surprise you. t.co/4xn6XVGkpY

Methodology for comparing LLM output quality blindly

Martijn Verbove@verboveAI · #5

Most agent builders jump straight to vector search and skip procedural memory entirely. I built a local knowledge graph with MCP that handles exactly this, capturing what the agent did and why so it can reuse those patterns.

Explains knowledge graph + MCP for agent memory reuse

Peter Prins@PeterPrins10AI · #6

LLM model switching comes at cost. Every model LLM has it's own quirky outcomes. Learning these quirks and adjusting your instructions takes time. Therefore, there is a real cost to switching models. The code below is written by GPT-5 on Auto with the new Codex plugin. t.co/HCcc442n3q

Explains hidden cost of switching models

Peter Prins@PeterPrins10AI · #7

Fable is the last model you need for programming. We don't need the model to be smarter. We need it to be less token hungry. There should be enough headroom for Anthropic in post-training and the system prompt to make the model perfect.

Token efficiency matters more than capability for coding

Peter Prins@PeterPrins10AI · #8

Using Grok 4 Fast today - So far it's a better experience than GPT-5. Unfortunately, it's not implemented in Cursor just yet. So you will need to set it up manually and use your own API keys.

Teaches setup method for using new LLM

Martijn Verbove@verboveAI · #9

Most "AI agents" are just prompts with extra steps. That's not a dunk. That's the whole point. A Managed Agent is three things: → A prompt. The instructions you'd type into ChatGPT anyway. → Tools. Connectors to Slack, Jira, Intercom, Confluence — so it can fetch its own context. → Skills. Reusable playbooks for how the work gets done. That's it. No magic. The hard part isn't building the agent. It's knowing where to point it. And here's where most teams get it wrong: they point it at reports. Daily summaries. Weekly digests. Dashboards nobody reads. It feels productive. It's mostly noise. The real wins are upstream — at the moments where someone is spending an hour collecting context to do five minutes of work. Five places agents actually earn their keep: • Audit your docs against what support actually told customers this week • Auto-build the internal FAQ from repeated Slack questions • Enrich existing Jira tickets with org-wide context — don't open new ones • Sweep Slack + Intercom + Confluence for stale documentation • Draft the customer follow-up while you're still in the room Notice the pattern? None of them are reports. All of them close a loop a human is currently closing by hand. The architecture (managed in Claude vs. self-hosted with the SDK) matters less than where you point it. Swipe through ↓ for the full breakdown.

Explains managed agents + lists 5 specific high-ROI use cases

Martijn Verbove@verboveAI · #10

People forget AI is mainly a competitor to software, which has a very standardized pipeline. It can’t just be run wild. Even AGI can’t just “figure out” a business on its own. Humans are AGI too. We still need context, skill-building, and oversight to become useful inside a complex company. Same for AI. And most real commerce is physical anyway. Software is an enabling layer. AI will be too.

Explains AI as standardized layer needing context

Martijn Verbove@verboveAI · #11

"replace employees" is bait. the real thing is decision density + shipping speed. contrarian take: agents won’t replace employees. they’ll replace companies with individuals. biggest opportunity you’d bet on in the next 6 months? drop 1.

Teaches agents enable individuals at company scale

Martijn Verbove@verboveAI · #12

the biggest risk with ai isn't that it takes your job it's that your job never delivered an outcome to begin with ai is already better at reports, emails, and booking meetings if that's the job, that's the warning

Teaches insight about AI's actual impact

Martijn Verbove@verboveAI · #13

Everyone wants AI to think for them. It doesn't think. It reads, writes, and follows steps. The companies winning with AI pointed it at their most boring work. Copying data between systems, turning research into summaries, running content from draft to publish. Give it the full pipeline with clear inputs and outputs and it'll outwork your whole team.

Explains AI capabilities and winning application patterns

Martijn Verbove@verboveAI · #14

Experience is to people what context is to LLMs.

Conceptual insight comparing experience and context in AI

Martijn Verbove@verboveAI · #15

"Everyone talks about how AI will replace jobs in the future, but no one talks about how the decisions you make about AI adoption right now are already determining which jobs survive."

Insight about timing decisions determining job survival

Martijn Verbove@verboveAI · #16

AI transformation 101: - Find the process bleeding time (bottleneck) - Map where the friction actually lives (discovery) - Pull signal from your tools - Doczen does this fast (diagnosis) - Build a prioritized roadmap with real ROI attached (direction) - Ship automation that sticks, not decks that don't ($) Stop waiting on consultants.

Teaches 5-step AI transformation methodology with real ROI focus

Martijn Verbove@verboveAI · #17

95% of enterprise AI projects deliver ZERO measurable ROI. Not low ROI. Zero. MIT looked at 300 deployments, interviewed 150 leaders, surveyed 350 employees. The needle didn’t move. Meanwhile companies dumped $30-40B into “AI” last year and mostly lit it on fire. And everyone’s still hiring “Head of AI”. Why? Most companies don’t have an AI strategy. They have AI anxiety. They see a competitor’s press release, panic, and spin up 10-15 pilots with: • no success criteria • no owner • no kill switch That’s not innovation. That’s expensive theater. Comment “Get Shit Done” and I’ll DM you a free assessment of where AI can actually drive ROI in your business.

Enterprise AI fails without strategy; why and fix

Martijn Verbove@verboveAI · #18

Claude just prompted me to import cookies from Chrome It's a more fundamental shift to using web services then we currently realise AI clients are the user interface saas/sites will: - transform from interfaces to capabilities - elements on webpages will become widgets - software will become proactive in reaching out to you

Explains how AI clients transform software interfaces

Martijn Verbove@verboveAI · #19

What excites me about this is the participation angle. Right now barely anyone even has access to AI, but zoom out — most people on earth have never had access to a good lawyer, a financial advisor, or top-level education. When cognition gets cheap enough, all of that opens up. And honestly as a builder it just becomes way more fun — instead of grinding on one problem for two years, you can tackle ten and actually bring them to market at price points that work for way more people.

Explains how cheap cognition opens underserved markets

Martijn Verbove@verboveAI · #20

LLMs are not intelligent. I know that sounds weird. But hear me out. Birds are intelligent. Dogs are intelligent. Dolphins are incredibly intelligent. None of them have language as we know it. What set humans apart wasn't being smarter. It was that language let us build on top of other people's discoveries. You don't re-invent calculus. You read Newton. Language is the result of intelligence, not the source of it. So what does an LLM actually do? It reverse-engineers intelligence from language. Every token is compressed human knowledge, not thought. That's why context matters more than the model. The intelligence is already in the weights. Your job is to direct it. The people building the best context and skills will 10x the gap over everyone else. Not because they have better AI. Because they know what to feed it.

Explains LLM mechanism and importance of context

Peter Prins@PeterPrins10AI · #21

Programming with AI is full of surprises. Sometimes it astonishes me. Complex tasks, perfectly one shotted. Great code. Then I ask it to do something easy, even for a junior dev, and the code AI generates is just horrible.

AI code quality is unpredictably variable by task type

Peter Prins@PeterPrins10AI · #22

Is Apple the secret winner of the AI revolution? Closing a billion doller deal with Google to create an Apple specific Gemini model, may be the most cost effective way to bring state-of-the-art AI to Apple devices without huge CAPEX investments.

Licensing AI more cost-effective than building in-house